00:07 All right.
00:07 Good afternoon,
00:08 everyone.
00:09 Welcome back to the sessions.
00:11 My name is Shamilawe.
00:12 I work for the World Bank,
00:14 and I'm very happy to welcome all of you to our session today
00:18 on norms and other constraints to women's economic inclusion.
00:23 We'll have 4
00:24 really amazing presentations over the next 1 hour.
00:29 Where
00:29 our speakers will tell us about the role of norms,
00:33 the role of networks,
00:34 and the role of neighborhoods
00:36 in limiting access to opportunity for women.
00:39 Uh,
00:40 I will introduce
00:42 each of the four speakers,
00:44 and then they will speak for about 15 minutes or 14 minutes each as they desire.
00:51 And then we'll turn to all of you for questions and answers.
00:54 So our speaker
00:56 who's gonna speak first is Pamela uh Jaquila,
00:59 who's a professor of economics
01:02 at Williams College and a non-resident fellow at the CGD.
01:06 After her will be Anukriti,
01:09 who's a senior economist at the World Bank's Research Group,
01:13 and,
01:13 uh,
01:14 she's doing,
01:15 uh,
01:15 and before joining the bank she was assistant professor
01:18 at Boston College.
01:20 After Anu's presentation we will have Nishit Prakash,
01:24 uh,
01:25 give his talk.
01:26 Nishi is a professor of economics and public policy at Northeastern University
01:31 and
01:32 he has been a visiting
01:33 fellow at Yale,
01:34 Columbia,
01:35 and MIT.
01:37 Uh,
01:38 following Nishit we will have Rachel Heath who will give her talk.
01:41 Rachel is an associate professor in the
01:45 economics department at the University of Washington,
01:48 uh,
01:48 and she did,
01:49 uh,
01:50 I did not know you did a postdoc at the World Bank's research department.
01:53 So
01:54 let's start
01:55 with Pamela,
01:56 but before I start,
01:57 uh,
01:58 handing over you,
01:58 uh,
01:59 the mic to you,
02:00 uh,
02:01 I've been asked that,
02:02 uh,
02:02 for all our online
02:04 viewers.
02:05 You are fully able to participate in the questions and answers uh during the panel.
02:11 Send your questions through YouTube,
02:14 through the LinkedIn live stream,
02:16 or email
02:17 events at CGDev.org.
02:21 And for folks in the room,
02:23 please keep your cell phones and other devices on silent.
02:27 Pamela,
02:27 over to you.
02:33 Here it goes.
02:36 OK,
02:36 hi,
02:37 thank you very much for joining us today.
02:40 Um,
02:40 I,
02:41 it's a real pleasure to be back here at CGD.
02:44 Um,
02:44 so what I'm going to do in this talk is I'm not going to present one specific paper.
02:49 I'm going to talk about,
02:51 uh,
02:52 some work I've done,
02:53 most of it joint with Dave Evans at the IDB,
02:56 uh,
02:56 reviewing the literature on
02:59 early childhood interventions in low and middle income countries.
03:03 And so what I'd like to do today is to talk about
03:06 the intersection.
03:07 Between
03:08 early childhood and the constraints on women
03:12 and gender norms
03:13 and sort of present some
03:15 regularities of that body of literature and talk about how
03:19 they relate to norms within the household and how thinking
03:23 about them in terms of norms within the household can
03:25 change the way that we view the results in this space
03:29 and so I want to start.
03:31 Just to motivate a little bit,
03:32 there are a few things that we know as points of departure.
03:35 So the first is that there is
03:38 tremendous gender inequality both in high income
03:41 countries and in low income countries,
03:44 in the workplace and in the home.
03:46 So
03:47 in both rich and poor countries,
03:48 women are less likely to be in the labor force
03:50 than men in almost every country around the world.
03:53 And for women who do work,
03:54 they often receive lower pay for comparable work.
03:59 And so,
04:00 and this is true,
04:01 you know,
04:01 this varies
04:03 across the income distribution,
04:04 but this is broadly true around the world.
04:07 And it's,
04:07 we know that a big piece of this
04:11 gender gap,
04:12 and this builds on some of the research that we saw this morning,
04:15 a big piece of this gender gap is about women's unequal care work burden in the home.
04:20 So
04:21 women
04:22 do most of the childcare around the world
04:24 and obviously all of the pregnancy and childbirth,
04:28 and they
04:30 that.
04:31 This places constraints on the types of jobs that they can take.
04:34 This places constraints on how focused on work they are
04:38 and on their profitability.
04:39 And again this is true in low income settings and also in high income settings.
04:44 Now at the same time
04:46 we as development economists,
04:48 we know that early childhood,
04:50 which
04:51 includes in most countries a period where children are not yet in
04:55 school,
04:55 is a really critical,
04:57 critically important time for making investments in human capital.
05:01 Contribute to
05:03 your
05:03 ability to learn,
05:05 meet your developmental potential,
05:07 and you know,
05:08 be productive in the workforce throughout your life course.
05:10 And so we are very interested in a broad class of interventions that will get
05:16 that will increase the amount of
05:19 investment in particularly poor and vulnerable
05:21 children's human capital early in life,
05:23 and we know that this,
05:24 the failure to do this can create poverty traps
05:26 both for households and for countries as a whole.
05:29 So this creates this tension where women bear a disproportionate burden
05:34 of care work responsibilities in the home,
05:37 and yet we as development economists actually want
05:39 households to do more with their young children,
05:41 and that can make it,
05:42 that can create problems where we risk exacerbating
05:46 these types of gender inequalities in the name of
05:48 relaxing this poverty trap.
05:50 And so what I want to do today is talk
05:52 a bit about what we've learned from the literature on
05:54 ECD interventions and in particular what we have learned about
05:58 women and about households from that body of work.
06:01 So this is,
06:02 this is a figure from a paper that I
06:05 wrote with Dave Evans and Heather Knauer,
06:06 and it's just showing you the sort of explosion of
06:10 literature.
06:11 Evaluating ECD interventions in low and middle income countries.
06:14 This is growing over time and what's in blue here
06:16 is that
06:18 until quite recently,
06:19 almost all of these evaluations focused exclusively on children.
06:23 So even though when we think about what's going on in early childhood,
06:26 it is
06:27 often
06:28 the households and particularly the mothers who mediate these interventions.
06:32 Make them effective by changing their behavior and investing more in their kids.
06:35 We have until very recently basically ignored
06:38 the impacts of these interventions on women
06:40 and also on
06:42 men in the household and other people in the household.
06:44 This is changing over time though,
06:46 and we increasingly now have enough of a body of work
06:49 that we can start to draw some broad lessons from it.
06:53 So I want to talk about
06:54 3 regularities and a little bit about how we can
06:57 see them in the context of norms within the household.
07:01 So the first regularity,
07:02 the first result that's coming out of this
07:04 literature is about the impacts of center-based childcare.
07:08 So when I talk about center-based childcare,
07:10 I mean both daycare for children kind of ages 0 to 2
07:14 and preschool,
07:16 whether it's academic,
07:17 preschool,
07:17 pre-primary education and
07:19 through the government or informal or private pre-primary.
07:23 So
07:23 there are a couple of regularities that
07:26 from this literature that we can now be pretty confident about
07:30 that we
07:31 would not have known in advance would be true.
07:33 And so the first is that even though there are
07:35 ongoing concerns about the quality of daycare and pre-primary.
07:39 Low and middle income countries,
07:41 in general,
07:42 these types of programs,
07:43 center-based care,
07:44 is good for child development.
07:46 It varies depending on the counterfactual,
07:48 but in broad terms,
07:50 these policies are either weakly good
07:52 or or significantly substantially good for kids.
07:57 This,
07:57 and they also tend to in many contexts increase women's labor supply.
08:03 So this suggests that these types of center-based policies,
08:05 and this came up in the earlier discussion this morning,
08:08 that these types of center-based daycare and
08:10 preschool interventions may be win-win policies,
08:13 but a regularity that's coming out as we build
08:16 the evidence base on these interventions is that,
08:19 uh,
08:20 the impacts on women are not necessarily.
08:23 As large and as consistent as we might expect,
08:25 and one thing that we do seem to see in a lot of contexts,
08:29 a growing number of contexts,
08:30 when we actually look at it,
08:32 is that giving households access to childcare is also
08:35 increasing men's labor force participation and men's income.
08:40 Now this is interesting,
08:42 because it isn't the case that what is
08:44 happening is men are doing less childcare work.
08:49 When the kids go into childcare,
08:50 and the reason we know that's true is that in most of these settings,
08:53 men aren't doing any childcare to begin with,
08:56 OK,
08:56 and so what's happening here
08:58 is that we can think about this in terms
09:00 of some sort of re-optimization within the household,
09:02 but when a household gets access to childcare,
09:04 they often take advantage of it,
09:06 and this changes how all the members of the household allocate their time.
09:10 And this could be good.
09:11 This could be a great,
09:12 you know,
09:13 economic theory could predict that this is
09:14 a re-optimization that is good for everyone,
09:17 but it also raises concerns about whether what is really happening
09:20 is that when women get time freed up from childcare,
09:23 they're just forced to spend it on other domestic tasks that the husbands don't do.
09:28 And so this raises an issue
09:30 that we.
09:31 Only now are being able are able to even think about speaking to,
09:36 which is,
09:37 do these interventions actually make women better off and how do they
09:40 change allocation of tasks and allocation of domestic work within the household
09:44 and the problem in answering this question in theory as we.
09:47 See more and more expansions of childcare,
09:49 we could answer this,
09:50 but most of the time we don't ever look.
09:52 So a vanishingly small number of evaluations of these
09:55 types of interventions actually measure outcomes for men.
09:58 So basically we just don't know what they're doing.
10:01 We don't know what's going on.
10:03 OK,
10:03 so that's
10:04 regularity in puzzle number one.
10:06 I think this is,
10:07 uh,
10:07 so this leads to this question of do these types of interventions,
10:10 which we hope would be win-win,
10:12 actually make
10:13 women better off,
10:14 or are they just being stuck doing other types of domestic work,
10:17 uh,
10:18 once their children are in childcare?
10:21 OK.
10:22 The second point I want to make is
10:24 about another class of early childhood interventions.
10:27 These are,
10:27 uh,
10:28 it's actually two types of interventions,
10:30 group-based parenting classes for women
10:32 and also home visits from child development professionals.
10:35 This is a type of intervention that for a long time
10:38 has been recognized as something that can be very valuable to,
10:42 uh,
10:42 to children,
10:43 to increase the stimulation that they get,
10:45 translating into benefits in terms of their human capital and their income.
10:49 What's interesting when you look at the whole literature,
10:51 all of these evaluations,
10:53 is that there's actually really robust evidence
10:56 that these types of interventions in a
10:58 wide variety of country contexts improve women's
11:01 mental health and their subjective well-being.
11:04 So
11:05 this is something that you know we've seen it,
11:07 we've seen it in South Asia,
11:08 we've seen this in Latin America,
11:10 we've seen this in Africa
11:11 for both home visit interventions and group based parenting classes.
11:16 The question that I ask about this literature,
11:19 why I think this is really interesting is the question of why this is the case.
11:23 So one simple story is that parenting education improves women's self-efficacy
11:28 in terms of how good of a mother they are,
11:30 and that's really great,
11:31 and that could be
11:32 part of the story.
11:33 Or the whole story,
11:34 but what we know is that
11:36 women,
11:37 uh,
11:37 young mothers in many low and middle income countries
11:40 have very low,
11:42 very weak social networks.
11:44 This is particularly true in regions of with
11:46 patrolocal norms and restrictions on women's mobility.
11:50 And if we look,
11:52 I've had conversations with a number of you.
11:54 When we look at the data,
11:55 the rates of depression among young women and
11:58 mothers in low and middle income countries are often
12:00 staggeringly scarily high.
12:02 And so what I think
12:05 could explain this is that these types of interventions that were designed
12:08 to be about improving mothering and parenting and be childhood interventions are.
12:13 Equally valuable as interventions that facilitate the creation
12:16 and the strengthening of women's social networks,
12:19 and I think that's again something that
12:21 we
12:21 don't yet really have the data that would allow us to look at because it
12:25 isn't something we've been looking at as
12:27 an outcome from these types of interventions,
12:29 but it's something,
12:30 a way in which these interventions may be equally effective at outcomes we
12:33 didn't pay attention to at all that they weren't designed to target.
12:37 OK.
12:39 My last regularity uh is about fathers.
12:43 So fathers,
12:45 they're there,
12:46 you know,
12:47 they're instrumental in the in the creation of children,
12:51 and yet when it comes to what they do,
12:53 it's often staggeringly little.
12:55 So there's an interesting and you know I say this
12:58 with all due respect to the fathers in the room,
13:00 including the one who's the father of my children.
13:03 There are many fathers who do lots of things,
13:05 uh.
13:06 But
13:07 there's a rich literature in demography
13:09 and anthropology and economics that shows that
13:11 what's interesting in low and middle income
13:13 country contexts is that when fathers are absent
13:15 there is often surprisingly little impact on child survival and child outcomes,
13:21 and
13:21 the data we have on what what fathers do in terms of early childhood stimulation,
13:26 the best data we have comes from UNICEF's.
13:28 Mixed surveys,
13:29 it shows that fathers do
13:31 very,
13:32 very little engaging with young children.
13:34 They do less than mothers in almost every country in the world.
13:37 They also do less than
13:38 other adults who happen to be around.
13:41 They do very little of this kind of active parenting
13:43 and so recently there have been a number of interventions
13:46 that have tried to change this,
13:47 that have tried to get fathers involved,
13:50 uh,
13:51 and so.
13:52 Uh,
13:54 And so this is,
13:55 I mean this is
13:56 an area where
13:59 until 10 years ago there was,
14:00 I think,
14:01 one
14:02 published evaluation of an intervention
14:05 about parenting that targeted fathers in low and middle income countries.
14:08 The literature has expanded rapidly
14:11 so that now there are,
14:12 you know,
14:13 15 perhaps.
14:14 It's still a very small literature,
14:16 but it's growing very rapidly.
14:18 And we're starting to again see a couple of regularities about it.
14:21 And so the first
14:23 is that it is very difficult to get fathers to even show up for these interventions.
14:28 So for every study
14:30 that successfully engages fathers and changes their parenting knowledge,
14:34 there are 2 studies that got bogged down in the field and never
14:38 got to the results stage because you simply couldn't get dads to come.
14:42 Uh,
14:43 and then the second regularity is that
14:46 a well-designed intervention in the right
14:48 context often can change father's knowledge,
14:52 but that this almost never translates into changes in behavior.
14:56 And so it is
14:58 very,
14:58 very difficult,
14:59 very,
14:59 very costly to get fathers engaged,
15:01 and when we do get fathers engaged,
15:03 what's interesting is we see that
15:05 when we get fathers,
15:06 uh,
15:06 when we change fathers' knowledge,
15:08 it spills over a little bit onto mothers,
15:10 and that can change mother's behavior in some contexts,
15:13 but.
15:14 Fathers' behavior
15:16 is very difficult to move,
15:18 and when we focus on fathers,
15:19 we risk,
15:20 in fact,
15:21 missing an opportunity to engage women
15:23 in what has traditionally been a female dominant dominated space parenting.
15:28 And so
15:29 as this literature grows,
15:31 of course,
15:31 hopefully we will continue to explore
15:34 new types of interventions,
15:36 but for me this raises the question of whether
15:39 we should even be trying to work in this literature,
15:42 whether we should be trying as hard as we are.
15:45 To get fathers more engaged
15:47 and whether if we do so that's actually constructive or whether we risk
15:51 bringing fathers and their opinions and their uh you know unequal gender norms
15:57 into the space of parenting which has traditionally been the domain of women.
16:01 So for me these are three different
16:03 puzzles about regularities we see in the literature
16:08 that.
16:10 Are somewhat surprising,
16:11 but we can see them in the context of unequal norms within the household.
16:15 These are questions that we
16:17 have the potential to answer.
16:18 So there's a huge,
16:20 a huge set of evaluations,
16:22 and to the,
16:23 to some extent some of these questions are things that one could go
16:25 back and answer if you looked at the data in the right way,
16:27 if you looked.
16:28 For the right types of heterogeneity,
16:30 but we haven't answered yet,
16:32 uh,
16:32 and hopefully moving forward as we continue to
16:35 focus on early childhood as a really important domain
16:39 for development interventions,
16:40 we'll be better able to try to
16:41 go into it with this model in mind of unequal norms in the household
16:47 and women's,
16:48 uh,
16:49 women's the barriers they face in
16:51 building their social networks and exerting autonomy
16:53 and think about,
16:54 uh.
16:55 How that would translate into the set of outcomes that we measure and
16:59 how that changes the way we think these interventions are likely working.
17:02 OK,
17:03 I will stop there.
17:04 Thank you very much.
17:06 Thank you.
17:08 I know I would too.
17:17 I'm not sure if this is moving.
17:20 It'll show up,
17:21 it'll show up.
17:25 It's quite a delay.
17:27 15 seconds.
17:30 That is.
17:31 Thanks Pam for that segue and now we're going to move
17:33 to a slightly different aspect of gender equality which is,
17:37 uh,
17:37 women's access to social networks.
17:39 So
17:40 you know we all know that social networks are significantly important for various,
17:44 you know,
17:45 dimensions of well-being.
17:46 So it's,
17:46 you know,
17:47 we all know that we,
17:48 we get a lot of information about jobs,
17:51 about business opportunities.
17:52 From our networks,
17:54 you know,
17:54 they
17:56 help us smooth consumption,
17:57 they insure us,
17:58 and these are especially important in countries and
18:00 contexts which have you missing markets or missing
18:05 institutions.
18:05 So today I'm going to focus on
18:08 a low and middle income country which is India,
18:10 where I have some research on women's social networks.
18:13 And what the literature has shown us is that
18:15 women typically have fewer social connections than men,
18:18 and especially when you look at their social connections outside the household.
18:22 So,
18:22 and if you look at interactions that women have with other people on
18:26 more private and typically stigmatized topics such
18:29 as family planning and reproductive health,
18:31 these interactions become even smaller.
18:33 Um,
18:34 and,
18:34 and this combined with the fact that there's a significant homorpholy by gender,
18:38 by which I mean that women tend to have connections
18:41 with other women and men tend to have connections with other
18:44 men,
18:45 then puts women at sort of a double disadvantage in terms of access to networks,
18:49 access to information,
18:50 and so on.
18:52 So
18:52 what we do in,
18:53 uh,
18:54 uh,
18:54 you know,
18:55 so,
18:55 so let me,
18:56 before I go to the two papers that I'm going to talk about,
18:58 give you a little bit of context since I'm going to talk about women in India.
19:01 Um,
19:02 so this is probably not a surprise to this audience that women in India are
19:06 significantly constrained as far as mobility is concerned.
19:09 So if you look at data from demographic health.
19:12 Surveys,
19:12 a very
19:13 high percentage of women report,
19:15 especially married women,
19:16 that they are not allowed to visit places outside the home alone,
19:20 so they always have to have somebody accompany them,
19:22 and you know there's some numbers here,
19:24 so 60%,
19:25 for instance,
19:25 are not allowed to go alone to the market,
19:27 uh,
19:28 health facility,
19:28 or places outside the
19:30 the village or community.
19:32 And this is correlated with the fact that a lot of them practice,
19:36 you know,
19:36 covering their head and faces through Pha and Kunat.
19:40 They are not engaged with the labor market,
19:42 so there's significantly,
19:43 uh,
19:44 you know,
19:44 large gender gaps in labor force participation,
19:47 and then that act basically means that women are less
19:50 likely to go outside of the home to even work.
19:53 You might say that we have access to digital technology,
19:56 so maybe women can engage with other people through phones,
19:59 but if you look at the mobile gender gap in India,
20:02 it's also quite high.
20:03 So in urban areas women are more likely to obviously have phones,
20:05 but
20:06 overall you know only 33% of Indian women have access to a phone.
20:10 So this makes for a very socially isolated existence
20:15 and.
20:15 Can obviously have negative consequences which I'm going to talk about.
20:19 Uh,
20:19 one thing that I hear a lot about,
20:21 uh,
20:21 women's social networks is access to self-help
20:24 groups or other sort of collectives.
20:26 Uh,
20:26 while that's a very important way in which women can engage with other people,
20:30 especially other women,
20:31 uh,
20:31 if you look at the data,
20:33 only 20% of women say that they are part of a group or a collective.
20:37 So while that's a promising.
20:38 Avenue that really does not serve
20:39 all women
20:41 and uh lastly,
20:42 the interactions that women do have with other people are heavily regulated,
20:47 so especially family members like their husbands
20:49 or mothers-in-law that I'm going to talk about a lot more in detail
20:53 are regulating who women talk to and whether they
20:56 even have access to places outside the home.
20:58 So for instance,
20:59 22% of women.
21:00 In the demographic Health Survey of India reported that they
21:03 are not permitted to even meet their female friends,
21:06 right,
21:06 so
21:06 this is a context,
21:08 and this is more so in rural India,
21:09 maybe in certain parts of India,
21:11 and so it's not,
21:12 you know,
21:12 everywhere,
21:13 but it does tell us that there is a significant degree of social isolation
21:17 and now what the consequences of that are,
21:20 how can we correct it is something that I'm going to talk about.
21:24 So,
21:24 uh,
21:25 you know,
21:26 so why,
21:26 you know,
21:27 of course we can see that this is problematic,
21:28 but the context that I'm going to talk about today
21:31 is women's access to family planning and reproductive health services,
21:34 and that's a very
21:36 heavily gendered sort of topic because
21:38 even though family planning is important for both men and women in this context,
21:42 access to family planning is especially more,
21:45 it's considered more a woman's sort of job to,
21:47 you know,
21:47 figure out whether to use family planning or not,
21:50 uh,
21:50 so you know some people say that OK,
21:51 women may not have access to their own.
21:53 But what about their husbands,
21:54 right?
21:55 So their husbands are well connected and maybe that helps.
21:57 So while it's true that yes,
21:58 that can help,
21:59 but when you talk about these gendered
22:01 sort of topics,
22:02 there is very little interaction between men and women.
22:05 So if men don't talk to other women about family planning and reproductive health,
22:09 then it's unlikely that that information is going to
22:11 spread through husbands and go to their wives,
22:13 right?
22:14 So
22:14 this can have very severe negative consequences for
22:18 for women's information even about family planning
22:21 or even access to family planning.
22:23 And uh another uh sort of so husband
22:25 networks in that sense are not a perfect substitute
22:27 and
22:28 the importance of family members,
22:30 as will become clearer in uh in a slide
22:32 is also very important here because these family members,
22:35 as I said,
22:36 can be barriers or enablers,
22:38 and if they are barriers then that can.
22:39 Of,
22:40 you know,
22:40 be an additional
22:42 and if they have reasons to constrain women from going outside the house because of,
22:45 let's say,
22:46 norms about women's mobility
22:48 or because they have different preferences or incentives to
22:52 prevent women from going out,
22:53 then that can sort of,
22:54 you know,
22:54 exacerbate the problem that I'm talking about.
22:58 So,
22:58 uh,
22:58 I'm going to share some findings from,
23:01 uh,
23:01 a project that is the Jaunpur Social Network Study,
23:04 uh,
23:05 which we conducted along with my co-authors,
23:07 uh,
23:08 Kalina Here Almanza at UIUC
23:10 and Mahesh Kara at Boston University,
23:13 and we basically went to,
23:15 uh,
23:15 one of the Indian states which is Uttar Pradesh.
23:17 It's the most populated state in India,
23:19 uh,
23:19 as some of you know,
23:21 uh,
23:21 it's,
23:22 if we only looked at population,
23:23 it would be the 5th largest.
23:24 Largest country in the world if it were a country,
23:26 so this is a very big part of the world,
23:29 and we collected data from 28 villages in Jaunpur,
23:33 and we surveyed 671 women
23:36 who were,
23:36 and we had certain criteria we adopted,
23:39 so these had to be married women,
23:41 uh,
23:41 relatively young because we were talking about family planning,
23:43 so 18 to 30 years old,
23:45 and they had to have at least one child at
23:47 baseline because otherwise family planning take up is very low.
23:50 And we collected data on women's social interactions
23:54 and so we we knew that women would
23:56 obviously engage with their husbands about family planning
23:59 and potentially also their mothers-in-law,
24:01 so we asked them about
24:03 people other than these two individuals who they engage with on various topics.
24:08 So
24:08 you'll hear me say something called general peers.
24:10 So these are.
24:11 The people that women engage with on any sort of issue,
24:15 for instance,
24:15 children's illness,
24:16 schooling,
24:16 health,
24:17 work,
24:17 or financial support,
24:18 so these are just people that you talk to about
24:21 various things.
24:22 And then we also specifically asked about
24:24 close peers because it's a more private topic
24:26 about women,
24:27 about people who they talk to about fertility,
24:30 family planning,
24:30 and reproductive health.
24:32 And what we find is that in both these
24:34 dimensions our sample women were very highly socially isolated,
24:39 so an average woman in our sample said she only engages with two other people
24:44 other than her husband and mother-in-law about anything,
24:47 and this is two people in the entire district where she lives,
24:49 right?
24:50 So this is contrary to the image we might have that all women have,
24:53 you know,
24:53 many other females.
24:54 Friends,
24:54 that's not the case.
24:55 Uh,
24:55 and if you focus on close peers,
24:57 so these like people with whom you have these private conversations,
25:01 that becomes even less.
25:02 So just one person on average.
25:04 In fact,
25:04 one third of our sample said that they do not have any close peers in their district,
25:09 and 22%,
25:10 uh,
25:11 uh,
25:11 don't have any close peers anywhere irrespective of your district or elsewhere.
25:17 The other characteristics we found was that most of the people
25:20 that they did speak to tend to be their relatives,
25:22 right?
25:22 So these could be
25:23 they may either live inside the household
25:25 or maybe outside people like sisters-in-law.
25:27 So sisters-in-law tend to be quite important,
25:30 especially in this context,
25:31 and again,
25:32 almost everyone,
25:33 in fact,
25:34 100% of their social connections were other women,
25:37 um,
25:37 all of them were from the same religion
25:39 and 94% belong to the same caste.
25:42 So there's a lot of homophily by gender,
25:43 religion,
25:44 and caste in this context.
25:48 So the first paper we wrote from our baseline
25:50 data was about mothers-in-law and whether they have any
25:54 influence on women's access to social networks.
25:57 So what we find is that women who co-reside with their mothers-in-law
26:00 have 20% fewer close peers
26:03 in the village and 37 fewer close peers outside the household,
26:07 right?
26:08 And so this is a correlation,
26:09 and we also find that co-residence with mother-in-law significantly reduced.
26:14 Uses women's ability to access places outside the home,
26:17 so which is consistent with the fact that
26:19 you know they don't have access to networks.
26:21 Uh,
26:21 we did not find any such influence of fathers-in-law or sisters-in-law,
26:25 uh,
26:25 so it's not just that,
26:26 you know,
26:26 you have in-laws who prevent you.
26:28 So
26:28 mother-in-law in some in this context,
26:30 which I think is not surprising to South Asians,
26:33 uh,
26:33 is,
26:33 is a significant barrier,
26:35 uh,
26:36 and we also then,
26:37 you know,
26:37 try to sort of this is a correlation,
26:39 but we try to in the papers.
26:40 Show that this is actually a causal result
26:43 and then we were curious about why is
26:46 it that mothers-in-law are this restrictive influence.
26:49 So in the context of family planning,
26:50 what we find is that it has to do with the
26:53 discordance in the fertility preferences of
26:55 the mother-in-law and the daughter-in-law.
26:56 So if the mother-in-law wants her daughter-in-law to
26:59 have more children than the daughter-in-law wants,
27:01 we find that this negative influence is
27:04 stronger
27:05 if the mother-in-law approves.
27:06 Disapproves of family planning,
27:08 then this negative influence is stronger,
27:10 and if the husband is away,
27:11 then again we find that this is stronger,
27:13 right?
27:13 So what this tells us is that the mother-in-law is worried about
27:17 the daughter-in-law
27:19 adopting family planning or learning family planning
27:22 contrary to what she wants her to do,
27:23 and then as a result she prevents her from
27:26 accessing people or you know places outside the home.
27:29 Now this is a problem because
27:31 in this context we have a high.
27:32 Unmet need for family planning.
27:34 So in our sample,
27:35 half of the women said that they don't want to have any more children,
27:38 but only 19% were using a method of family planning.
27:42 So
27:43 essentially what this means is that women who live with
27:46 their mother-in-law then have fewer close peers outside the household
27:50 are then less likely to visit places outside the home,
27:52 family planning clinics,
27:54 and use methods of modern contraception.
27:56 So that was the first result we find.
27:59 And then
28:00 what we did subsequently was design a randomized
28:03 control trial where we wanted to see how
28:06 can we circumvent this uh this negative influence
28:09 of the mother-in-law and expand women's ability to access
28:14 or use the support of their peers to access places outside the home.
28:17 So what we did was we,
28:20 we had an RCT where we split the sample into 3 groups.
28:23 So one group was the control group,
28:25 and then the remaining women.
28:27 Were given access to a voucher for family planning at a local clinic,
28:31 right?
28:31 So,
28:32 so
28:33 let me first describe the own voucher group,
28:35 what we call.
28:35 So own voucher group basically gets a voucher which enables,
28:38 gives them ₹2000 or $30 worth of family planning services.
28:43 They can use it for a period of 10 months at a local clinic.
28:46 And
28:47 in addition,
28:48 the second treatment group got the same voucher,
28:50 but also we told them that if you brought a friend to the clinic
28:54 that.
28:55 Friend will also become eligible to receive the same voucher,
28:57 right,
28:58 so basically the difference,
28:59 so in both cases the treated woman is getting exactly
29:02 the same incentive to go to the family planning clinic,
29:06 but in one case she's also able to leverage this friend voucher
29:10 to incentivize someone else to go with her,
29:12 right?
29:12 So we did not restrict who she can bring
29:15 with her to the clinic,
29:16 but
29:16 given that this is the context we're working in,
29:19 we think it's mainly going to be young women who are in need of family planning.
29:24 So what we find is that both vouchers
29:27 increased likelihood of visiting a family planning clinic,
29:30 so clearly financial incentives in this case or financial constraints matter,
29:35 and in both cases we find that they're more
29:37 likely to visit without their husbands and mothers-in-law,
29:40 right,
29:40 so,
29:40 so it reduces their dependence on them
29:42 to access the family planning clinic.
29:45 However,
29:45 we find,
29:46 and which we were quite happy to see,
29:48 is that the Bring a friend voucher was significantly more successful
29:52 than the own voucher for women whose mother-in-law
29:55 was more opposed to family planning at baseline.
29:58 So this suggests that having this ability to take a friend along enabled these
30:03 women to overcome opposition from their mothers-in-law
30:05 and access places outside the home.
30:08 Um,
30:08 in fact,
30:09 the own voucher,
30:10 which is typically what family planning programs do,
30:12 uh,
30:13 was completely ineffective for these women.
30:15 So without the support of this other peer to go with
30:18 to the family planning clinic,
30:19 they were not able to access it.
30:21 Uh,
30:22 and
30:22 you know,
30:23 consistent with this,
30:23 we find that modern method use and,
30:25 uh,
30:26 pregnancy rates also decreased for women who received the bring a friend,
30:29 uh,
30:29 voucher.
30:32 In addition,
30:32 so since the paper was also trying to improve women's access to social connections,
30:37 we find that our Bring a Friend voucher was
30:40 able to increase women's number of social connections,
30:43 especially those outside her home.
30:45 So basically what it meant was
30:47 if you already had some friends,
30:49 it improved your engagement with them
30:51 because now you maybe are more likely to talk to them about family planning.
30:55 You have this voucher,
30:56 or if you did not have any suitable peers,
30:59 you could go to a neighbor
31:01 and tell them about this and maybe in the process of
31:03 this form a social connection with them and discuss family planning.
31:06 So we do find that this effect is entirely driven by the bring a friend voucher,
31:10 which suggests that because even own voucher women could have done that,
31:14 you know,
31:14 but since they did not have anything to offer to the peer,
31:17 it was less effective.
31:19 Um,
31:20 and then lastly,
31:21 uh,
31:22 we also find consistent with the,
31:24 uh,
31:24 previous literature that having more peers
31:27 did improve,
31:28 uh,
31:28 women's,
31:29 uh,
31:29 stigma about family planning,
31:31 right?
31:31 So if peers can provide support,
31:32 for instance,
31:33 to counter stigma related to mental health
31:35 in a similar manner,
31:36 what we find is that
31:38 Bring a Friend voucher enabled women
31:40 to reduce the stigma they have about access to family planning.
31:45 Uh,
31:45 so what does this tell us about policy and research?
31:48 So first of all,
31:49 what we found in the process of writing this paper that
31:52 we really do not have much data on women's social networks.
31:55 So typically when we collect this,
31:56 it's at the household level,
31:57 uh,
31:58 or you may pick one person,
31:59 the head of the household,
32:00 and ask,
32:01 uh,
32:01 typically it's a him about the,
32:03 you know,
32:03 people they are connected to,
32:05 uh,
32:05 and so I have some work ongoing work with Ishani where we're trying to.
32:08 See what is the global cross country evidence on this,
32:11 but I think it's very,
32:12 uh,
32:12 you know,
32:13 important for us maybe to utilize these large data set,
32:16 uh,
32:16 that like DHS and so on to,
32:18 to add maybe a few simple questions that
32:20 can tell us more about women's social networks.
32:22 But putting that aside,
32:23 I think what this paper also tells us is that
32:26 uh
32:27 that we need to think about ways in which we can expand women's ability to.
32:31 Interact with other people.
32:33 Yes,
32:33 women's groups are one such option,
32:35 but maybe there are other ways we can leverage
32:37 or incentivize,
32:39 uh,
32:39 women to connect with
32:40 other women or even other men,
32:42 right?
32:42 And,
32:43 and in this case we find that sisters-in-law are,
32:45 uh,
32:46 are actually a very,
32:47 you know,
32:47 useful avenue,
32:48 uh,
32:49 given that one there is less like there's supposed
32:51 to be less stigma about interacting with family members.
32:54 Many of.
32:55 These sisters-in-law may either live with you or maybe in the same village as you,
32:58 so I think that's how to promote that engagement is something
33:02 that more research can be done on.
33:04 But of course you know there are,
33:07 you know,
33:07 we need to think about strategic interactions within households.
33:10 Maybe there is intra-household rivalry or there's intra-household bargaining
33:13 issues we need to think about with sisters-in-law,
33:17 but yeah,
33:17 I'll stop there.
33:20 I
33:21 I know.
33:24 The ship over to you.
33:27 It'll take about 15 seconds.
33:30 Just need to wait.
33:38 Doing a psych experiment
33:41 Thanks for the invitation and,
33:42 you know,
33:43 to be part of this great conference.
33:45 Uh,
33:45 and I was thinking about the order of the presentation and I,
33:48 I totally see why it makes sense because Anu sets the stage for
33:53 Another constraint that I'll be talking about today,
33:56 so today's talk is going to focus about sexual harassment in public space
34:00 and police patrolling.
34:01 I'll be talking about two of the papers
34:05 which is part of a larger agenda on this topic
34:07 of gender-based violence with Maria Mikhaila who is here,
34:12 Sophie Amaral and Girja Borkar at the World Bank.
34:14 So.
34:16 To
34:17 set the framework,
34:18 there are
34:20 4 key components.
34:21 The first being
34:22 what's the problem,
34:23 and I think for this audience it's very easy that
34:26 violence against women is a huge problem no matter which country you look at.
34:32 But
34:34 the biggest challenge not just being the problem,
34:36 it's it's the underreporting.
34:38 And
34:39 even though
34:40 we see these statistics and feel that this is a big problem,
34:43 it hasn't been very easy to kind of convince.
34:46 A lot of partners that,
34:48 hey,
34:48 this is a big problem because
34:49 the response is like,
34:51 oh,
34:51 if it's a big problem,
34:52 why don't we see that in the data,
34:54 right?
34:54 And second is,
34:55 well,
34:55 you
34:56 people don't report.
34:57 And second being,
34:58 uh,
34:59 if this is not a problem here,
35:01 it might be a problem in some other city or some other district.
35:04 So this is kind of like my engagement with a lot of the policymakers.
35:09 I'm going to talk about the problem
35:12 in two particular contexts.
35:14 There has been some really nice paper talking about the consequences.
35:17 I think that's fairly well established,
35:19 talks about
35:20 how these things affect mobility,
35:23 education,
35:24 female labor force participation,
35:25 and so and so.
35:27 Today's talk is going to focus a lot on the solutions.
35:30 On one hand,
35:32 I think there's lots of data about
35:34 gender-based violence and intimate partner violence,
35:37 but we don't know a lot about
35:40 sexual harassment in public space,
35:42 so that's completely kind of missing.
35:44 And
35:45 the business as usual is
35:47 we expect
35:48 women to report such crimes,
35:50 so we should be relying on the admin data.
35:51 That doesn't work because of the underreporting.
35:54 So today I'll be talking about a novel
35:58 method to measure sexual harassment in public space
36:00 and talk about evidence from two interventions,
36:04 both in India,
36:06 and it's worth pointing out that it's not as if policymakers or
36:10 police have not been thinking about this.
36:12 There has been lots of
36:14 Innovative,
36:15 I would say things that have been tried out in India,
36:17 including women help desk in Madhya Pradesh has also been studied by researchers,
36:22 all women police stations,
36:24 police patrolling,
36:26 women justice centers in Peru,
36:29 and
36:29 various ways to reduce the costs of registering crime.
36:33 It's not just like you have to physically walk to the police station,
36:35 which is very costly.
36:38 India started something called Dial 112,
36:40 so there has been,
36:41 I would say,
36:41 lots of progress in that.
36:43 And then it goes back to like you know when we started working on these papers,
36:47 we started thinking like
36:49 there's something missing,
36:50 there's something
36:51 much more important here.
36:52 It's more like structural reforms and
36:55 and
36:56 one aspect that we are going to talk about is going to be training programs,
36:59 so to what extent these training programs are effective
37:02 and then none of these things is possible without this I would say strong trust.
37:09 that you build with your partners,
37:11 including the police or various state police in India and in
37:14 many cases co-creating solutions which is kind of a lot easier to
37:18 convince them to scale up.
37:20 So
37:21 the,
37:21 uh,
37:22 so I'll be talking about two talks.
37:24 The first is going to be about,
37:26 uh,
37:26 an intervention that where we partner with Hyderabad City Police
37:30 in the Indian state of Telangana.
37:32 And
37:35 back then in 2014,
37:36 I would say it was pretty advanced to think
37:38 about a specialized police force which is called She Teams
37:42 with the sole objective of addressing gender sexual harassment in public space.
37:46 And the second is,
37:48 although
37:49 they don't quite overlap
37:51 because
37:54 we pretty much started our team working on the two projects almost at the same time,
37:58 but.
37:59 This is about a training program.
38:00 It's a very innovative training program that we co-designed with NGOs,
38:05 lawyers,
38:06 and the police,
38:07 which uses techniques from Theater of the Oppressed.
38:09 This is a very interactive,
38:12 expressive arts training program to study to what extent it
38:16 can reform police when it comes to gender-based violence.
38:19 So the first paper is co-authored with
38:22 Girija,
38:22 Sofia Amaral,
38:24 both at the World Bank,
38:25 Mika here.
38:26 Uh,
38:26 Anjani Kumar,
38:27 who,
38:27 uh,
38:28 was a police commissioner back then,
38:30 uh,
38:31 and Nathan Fiala,
38:31 who was my colleague at University of Connecticut.
38:34 So what do we do in this paper?
38:36 So it's a program where what we did is we partnered with the police
38:40 and we convinced them to vary the presence and the visibility of the police.
38:44 So in the interest of the time,
38:45 I'll skip some of the details of the intervention,
38:47 but what it did is
38:49 they already had a program where the police shows
38:52 up at these hot spots as an undercover,
38:54 means like they wear civil clothes
38:57 so that nobody can identify.
38:58 who they are and it's a lot easier to make arrests.
39:02 So intuitively I think they were spot on.
39:04 And then
39:06 it took us,
39:07 I would say
39:08 2 to 3 years to almost have this conversation and kind of convince them like,
39:11 look,
39:11 why don't we
39:14 put another arm which is about make them visible,
39:16 which is make them go in uniforms.
39:19 We tried this out in 350 hotspots and then
39:23 How do you measure street harassment,
39:25 and I think it's pretty obvious that we
39:27 could not have relied on administrative data.
39:30 So what we did is we trained enumerators who would go on the
39:33 hotspots and actually observe sexual harassment that's
39:36 happening and kind of code it,
39:38 and they did not know anything about the experiment.
39:41 And uh
39:42 then
39:42 we wanted to also understand,
39:44 you know,
39:45 I mean,
39:45 in fact,
39:46 uh,
39:46 this,
39:46 this was a part which uh this project happened uh in the COVID hit
39:52 and we could not collect a lot of data.
39:54 So we kind of went back like we had results and we
39:56 thought about like,
39:57 OK,
39:57 why do,
39:58 why are we finding these results
40:00 and uh since we did not end up doing.
40:03 A lot of data collection due to COVID.
40:05 We came up with this idea about the lab experiments,
40:06 and maybe we can do a lab experiment kind of kind of create very
40:10 similar scenarios for the police to understand why are we finding these effects,
40:13 and we had
40:14 kind of two
40:15 questions here that do you think police can detect these crimes?
40:19 And,
40:19 and I can't talk on behalf of my co-answers because I was like pretty
40:22 clear that they cannot detect this crime because it's a fast moving crime.
40:25 I was absolutely wrong
40:27 and why
40:28 they
40:29 don't sanction and under what circumstances they can sanction these crimes,
40:33 and we wanted to also study
40:35 their attitudes towards gender-based violence.
40:38 So
40:39 I'll skip the context,
40:40 but Hyderabad is no different,
40:42 probably maybe slightly safer than other parts of India.
40:46 So we did a survey where we found that 29%
40:49 face some form of sexual harassment and 87% take some kind of preventive measure.
40:54 So
40:55 as I said,
40:55 I would say it's a very I would say forward looking program.
40:59 They started in 2014
41:01 with this kind of core activity that they have a separate team.
41:05 This is
41:06 part of the police,
41:07 but it's a separate team,
41:09 very independent,
41:10 and what they did is they would do these undercover policing,
41:13 and it was kind of fairly monitored at the top.
41:17 And a key component of this patrolling was
41:20 they had at least one female officer
41:25 in the team,
41:25 so that's kind of one important aspect.
41:28 So as I said,
41:29 we spent almost 2 years
41:31 tweaking the program,
41:33 so they were expanding the program to another 350 hotspots.
41:36 So that's where we came in.
41:38 And we convinced them to have this kind of uniformed policing,
41:41 and on average these teams would visit hotspots 2 to 3 times in a week,
41:47 and each visit lasted around 15 to 20 minutes.
41:49 It's not a lot,
41:50 but I would say it was still quite a bit given that you almost had nothing before,
41:55 and this lasted 6 months.
41:57 That's the maximum time we convinced them to stick to a plan.
42:01 So this is,
42:01 uh,
42:02 I'll skip this part,
42:03 but,
42:04 and I'm going to skip the design and kind of talk about what are the questions.
42:08 So we wanted to study what's the impact of this program
42:11 and what's driving these results.
42:14 So these are like two things that we were interested in studying.
42:18 So the key finding
42:21 that was a big surprise,
42:22 I would say to us and also the police.
42:25 So whether it be it uniformed policing or undercover policing,
42:29 it actually had no effect
42:30 on aggregate measures of sexual harassment.
42:34 It was
42:34 kind of a really big surprise for the police commissioner who's also a co-author,
42:39 to
42:40 accept the result.
42:41 And then
42:43 we
42:44 looked into harassment by two categories which we
42:47 follow the Indian penal code and we kind of
42:50 divide this into milder sexual harassment and severe.
42:53 Milder means like whistling,
42:55 catcalling,
42:55 and by the way,
42:56 these are
42:57 illegal,
42:57 so there are Indian penal code and in fact it's punishable.
43:02 And then you have the severe one which is touching and groping,
43:04 and what we find is these uniformed
43:06 police patrolling reduces sexual harassment by 27%.
43:10 So for the severe form,
43:11 but nothing for the milder form.
43:13 And we also see this being reflected in women's behavior at the hot spot
43:18 when their
43:21 sexual harassment is happening,
43:22 the way they would approach this change,
43:24 so their preventive behavior change.
43:26 So this is like the two key findings
43:29 and
43:30 as I said undercover.
43:31 No effects whether be it mild
43:33 or severe.
43:34 However,
43:36 it's worth pointing out that the police were spot on because we did find more arrests
43:42 in undercover arm because it's easier to arrest,
43:45 but it did not translate into a reduction in sexual harassment.
43:48 Now what's driving
43:50 the result?
43:51 So
43:52 first,
43:52 it's being driven by deterrence.
43:54 So when you see an officer in uniform,
43:56 it's a pure deterrence effect.
43:57 So that's the key finding.
44:00 And the next one
44:02 was very important,
44:03 I would say as a researcher that the attitude matters a lot.
44:07 It means if these officers in the lab experiment what we found that officers who have
44:12 I would say more progressive or more harsher attitude towards gender,
44:16 so they actually act
44:18 on both severe and milder forms.
44:20 So that's kind of the two
44:22 reasons why we find these effects.
44:25 Now then goes
44:26 this other study which is in Bihar,
44:28 which also happens to be my home state.
44:31 This is with Mika,
44:32 Sophia,
44:33 and Girri,
44:36 and I would say.
44:37 is a project which I would say has taken a lot of our social capital,
44:42 so we did this in 12 districts
44:44 in Bihar.
44:46 The government changed.
44:47 Many police chiefs changed over time,
44:49 and here the key
44:52 the key intervention was to test a novel
44:56 training program because most of the training
44:58 program when these officers are hired.
45:00 They have this one time training and nothing happens after that.
45:03 So it's a it's a program which is more interactive.
45:05 It's not about like I'm going to present slides and you're going to attend like
45:09 the kind of training we take in colleges,
45:12 right?
45:12 So it's not like that.
45:13 It's very interactive
45:15 and we wanted to study the impact of the program
45:17 on both officers' technical and soft skills and uh spillover
45:22 so.
45:24 The key message is we targeted all the
45:26 key decision makers at the police station level.
45:28 That was the target,
45:29 and it was all male officers.
45:31 So
45:33 this pedagogy is the novelty here.
45:35 So
45:36 there are various things they try to target like technical skills,
45:39 truthfulness.
45:40 When a victim comes to complain,
45:42 do you believe the victim?
45:43 Victim blaming,
45:44 empathy,
45:45 attitude towards gender-based violence,
45:47 discrimination.
45:48 It's a pretty broad 3 day training program.
45:51 Now
45:53 I'm going to just
45:54 point out
45:55 one aspect.
45:56 So here it became a very emotional training program because officers are guided
46:00 through different ways in which their past behavior were harmful to the women.
46:04 So this became kind of like a fairly emotional part of the training.
46:08 This is kind of the pedagogy.
46:10 These are some pictures.
46:11 So this is like snake and ladder,
46:13 do's and don'ts as an officer.
46:15 Uh,
46:15 circle of influence as a police officer.
46:18 Uh,
46:18 and there's a bit of a story behind this handbook.
46:20 Like we piloted the training program,
46:23 and
46:24 after the pilot,
46:25 when we had the focus group meeting,
46:26 the officer said,
46:28 It was really fun,
46:29 but what do we do?
46:31 And
46:33 we came back and we're like,
46:34 wait a second,
46:34 we had a 3 day training program and he was saying we don't know what to do.
46:38 So we consulted senior police officers and
46:41 it was a very important lesson for me.
46:43 It's like.
46:44 You guys don't know what you're doing,
46:45 so with the police you have to give them an action book.
46:49 Without that it's not going to work.
46:50 So we created this kind of a really fun action book
46:53 which was about if someone comes,
46:55 this is what you're supposed to do translating inputs into outcomes.
46:58 And
46:59 I'll skip the experimental design and
47:02 some of the quotes
47:06 it felt like all the childhood memories were restored.
47:08 Some of them talked about why this training program should be one week and so and so.
47:14 And I'll just we find improvements in both technical and soft skills,
47:18 which was very encouraging,
47:21 and we also find
47:23 evidence of spillover,
47:24 means these junior female officers at the police station,
47:27 they were better treated
47:29 these junior officers were not targeted as a part of the program.
47:33 So just to kind of a way forward,
47:36 as I said,
47:37 the partnership
47:38 was very important because the state has
47:40 implemented this training program in their academy,
47:43 which means every new recruit
47:45 is going to go through this program,
47:47 and we just did the first batch in 2024.
47:50 And
47:51 the last part
47:53 which
47:55 We learned while working on this project in
47:58 Bihar was it's like a completely overlooked problem.
48:00 We expect police to do many things,
48:02 but we barely know what their daily challenges are.
48:06 Even I
48:07 was pretty clueless,
48:09 stress,
48:10 anxiety,
48:11 cholesterol,
48:11 blood pressure,
48:13 sleep.
48:13 We all know these things are important because there are
48:15 papers talking about various aspects and how this has implications.
48:19 But in this particular case we went ahead and we are asking them to do.
48:23 More things
48:25 without knowing what their constraints are,
48:27 and this is something that
48:30 me,
48:30 Girija,
48:31 Mika,
48:32 and Sophia and Lilith,
48:33 we have been talking about and we have collected data from
48:37 a few places in India
48:38 and this is a new agenda that we would like to pursue.
48:44 Well done.
48:48 All right,
48:49 Rachel.
48:50 Um,
48:51 all right,
48:51 well,
48:51 while I wait for the slides,
48:52 I'll just say,
48:53 uh,
48:53 thank you so much to Shani and the other organizers for kind
48:56 of creating this really interesting cohesive session and indeed whole day.
48:59 Um,
49:00 thanks to all of you for,
49:01 um,
49:01 for being here.
49:02 Um,
49:03 I'm excited to tell you about,
49:05 um,
49:05 uh.
49:06 Several of my projects,
49:07 uh,
49:07 like others,
49:08 I've kind of chosen several,
49:09 um,
49:10 uh,
49:10 several of my projects that fit together to kind of form kind of
49:13 a cohesive set of,
49:14 you know,
49:15 potential policy solutions,
49:17 uh,
49:17 that can work together to improve working conditions in,
49:20 in export manufacturing.
49:24 And the case study that I'm gonna use is the garment industry in Bangladesh,
49:27 which means that I do have to start with this
49:30 extremely sad picture that some of you might remember.
49:32 Um,
49:33 this was
49:33 the Rana Plaza factory.
49:35 Uh,
49:35 it collapsed in April 2013.
49:38 Sometimes people see the picture and say,
49:40 you know,
49:40 was there an earthquake?
49:41 No,
49:42 no,
49:42 there wasn't.
49:43 It just wasn't structurally sound.
49:44 It was never even supposed to be used for manufacturing,
49:47 um,
49:48 and it eventually,
49:49 um,
49:49 collapsed,
49:50 and,
49:51 um,
49:51 ultimately over 1000 workers were killed.
49:53 Which makes it the,
49:55 uh,
49:55 you know,
49:55 the biggest,
49:56 uh,
49:56 garment industry disaster in the history of the world
49:59 and the biggest disaster in any industry in the world since,
50:02 uh,
50:02 since 1984.
50:04 So just,
50:04 you know,
50:05 kind of a huge human toll as far as,
50:07 um,
50:08 loss of life,
50:09 um,
50:09 in surveys we've done afterwards,
50:11 just a random sample of workers,
50:13 you know,
50:13 up to 20% knew a worker that was seriously hurt or killed in Rana Plaza,
50:18 so just,
50:18 you know,
50:18 a huge,
50:19 uh,
50:20 a huge shock to the,
50:21 the industry.
50:22 And so in light of that
50:25 there was a lot of discussion after the um you know,
50:27 after the collapse,
50:29 are workers even gonna wanna keep working in the industry are suppliers gonna pull
50:33 out given that uh you know there
50:35 was such widespread disregard for worker safety that
50:38 factories were sending workers to work in a
50:40 building where they should never have even,
50:42 uh,
50:43 been working in the first place.
50:45 But as you see from,
50:46 um,
50:46 this graph that didn't happen in the least,
50:49 um,
50:49 the red line is,
50:50 uh,
50:51 is 2013.
50:52 When Rana Plaza happened,
50:53 Bangladesh is the,
50:54 um,
50:54 the heavy,
50:55 uh,
50:55 black line,
50:56 and so you see there was no trend break.
50:58 It just,
50:58 you know,
50:59 exports kept,
51:00 you know,
51:00 kept growing and growing,
51:02 and,
51:02 um,
51:03 ultimately Bangladesh has,
51:04 has grown into the,
51:06 um,
51:06 the,
51:07 you know,
51:07 the second biggest,
51:08 uh,
51:09 apparel exporter in the world.
51:10 China is on a different scale on the left,
51:12 but if anything it's decreasing its,
51:14 its exports.
51:15 So,
51:15 you know,
51:16 workers were still,
51:17 um,
51:17 going to the factories even in light of this huge tragedy.
51:21 And that makes sense because you know we also
51:24 know that the garment industry has had really important um
51:28 positive impacts on um on on workers and their their families.
51:33 So in some earlier work I've done with Mushfik Mubarak
51:35 we looked at the change in girls' um lives as the garment industry,
51:40 uh,
51:41 rolled out,
51:42 um,
51:42 and so what we.
51:43 Found is that the garment industry uh increased uh girls' education
51:48 and we,
51:48 we did that by comparing,
51:50 uh,
51:50 the,
51:50 you know,
51:50 enrollment rates of girls in villages proximate to
51:53 garment factories where girls could live at home
51:55 and commute to these garment factories compared to
51:58 other villages that were not proximate to garment factories
52:02 before versus after a garment factory opened.
52:05 Uh,
52:05 and so what you see in these,
52:06 uh,
52:07 in these graphs here is that there were particularly large effects on
52:10 younger girls who weren't eligible yet to work in the factories.
52:14 There might have been some dropouts among older girls,
52:17 not enough to have a negative effect.
52:19 You,
52:20 uh no age group here do we see a negative impact,
52:22 but there's,
52:23 um,
52:24 you know,
52:24 a less positive impact among,
52:26 among older girls,
52:27 and the effects were substantial.
52:29 So to take one age here,
52:31 um,
52:31 an 8 year old girl was 13% points more
52:34 likely to be in school after the garment industry,
52:37 uh,
52:38 came to her village compared to,
52:39 um,
52:40 another girl in a,
52:41 um,
52:42 in a non-garment proximate village.
52:44 And so,
52:45 you know,
52:45 kind of because the industry became so important,
52:47 we do a back of the envelope calculation that showed that,
52:50 um,
52:51 you know,
52:51 it's kind of some of the,
52:52 um.
52:53 Presentations this morning
52:55 pointed out Bangladesh is one of the countries that
52:57 has had a convergence in girls and boys' education.
53:00 We find that the garment industry was an important,
53:03 um,
53:03 contributor to that convergence,
53:05 um,
53:06 causing about 3% points nationwide of the increase in girls' enrollment.
53:11 Uh,
53:11 we also similarly found that the garment industry
53:13 led girls to delay marriage and childbearing,
53:16 um,
53:17 so kind of other positive impacts.
53:21 And then if we fast forward we can also see that it's not just future workers,
53:25 it's the current workers that have these jobs.
53:27 The garment sector jobs are providing
53:29 really important valuable sources of income.
53:32 So what I'm showing you here is some,
53:34 um,
53:34 um,
53:35 is uh is some ongoing work I have with Laura Boudreau and Waheed Rahman
53:39 where in the midst of the COVID pandemic in November 2020
53:44 we resurveyed a sample of workers who had been um active garment workers in 2017.
53:49 And so of course some of them were still working,
53:52 others of them,
53:53 them weren't
53:54 and so what I'm showing you here is the earnings of workers who men men versus women
53:59 who were still in the garment industry when we surveyed them in late 2020
54:03 compared to workers who had left the industry before the COVID pandemic,
54:07 uh,
54:08 before January 2020,
54:09 or workers who left after January 2020.
54:13 Um,
54:14 so I don't wanna claim that this is a causal impact of the,
54:17 of the garment industry.
54:18 Workers might have chosen to leave this industry because
54:20 they were less attached to the labor force,
54:23 but we've at least taken off the kind of most
54:24 obvious kind of selection here that we've only showed,
54:27 we're only showing you
54:28 workers who worked after leaving the garment industry.
54:31 So all these,
54:32 you know,
54:32 were employed,
54:33 um,
54:33 at some point.
54:34 And so what you see is the,
54:35 um,
54:36 among the women,
54:37 the blue line is the current garment worker line,
54:39 same,
54:40 same for men as well,
54:41 and so.
54:41 You see,
54:41 you know,
54:42 there's something of a dip among,
54:44 um,
54:44 you know,
54:44 in the kind of the real peak of the COVID pandemic,
54:47 um,
54:48 April,
54:49 uh,
54:49 April 2020 but the,
54:51 you know,
54:51 after a couple of months,
54:52 the,
54:52 um,
54:53 the,
54:53 the earnings rebound,
54:54 and they never lost the majority of their earnings.
54:58 That's in stark contrast to women that were,
55:01 uh,
55:01 you know,
55:02 employed but not in the garment industry
55:04 who really lost,
55:05 uh,
55:06 ultimately a very large share,
55:08 uh,
55:08 of their income.
55:09 Um,
55:10 and kind of,
55:10 you know,
55:11 similarly for these women garment workers' husbands,
55:13 they,
55:13 you know,
55:14 their,
55:14 their graph looks similar.
55:15 They also lost a large share of their income,
55:17 so the garment industry during the COVID pandemic
55:20 was providing a really important source of,
55:23 um,
55:23 of income support to the women working,
55:25 uh,
55:25 in the,
55:25 in the industry.
55:30 So that brings us to the question.
55:32 Um,
55:32 it's obviously a huge tragedy when,
55:35 um,
55:35 you know,
55:35 when kind of a factory collapses and kills many workers.
55:39 There's,
55:40 you know,
55:40 relatively high rates of other,
55:42 um,
55:42 you know,
55:42 injuries and um
55:45 um illness spread in the factories.
55:47 Um,
55:47 I've done some other work on estimating rates
55:51 of sexual harassment to tag on to vicious.
55:55 Uh,
55:55 work as well.
55:56 So I mean these are challenging places to work,
55:59 but they bring these important benefits to the women who are working
56:03 and,
56:03 uh,
56:04 you know,
56:04 the girls whose families invest in human capital for
56:07 them to get these jobs in the future.
56:09 So I think this brings up the key policy question
56:12 can policy
56:13 make these jobs better so that women can enjoy the,
56:17 you know,
56:17 the good parts of these garment jobs without,
56:20 um,
56:20 those,
56:20 you know,
56:21 hard working conditions and even tragic consequences.
56:26 So the first thing that I wanna show you is um is a paper by um.
56:31 From a project that I did with Laurent Bossavi and um Yun Cho,
56:35 who are both at the bank,
56:36 and so what we did was we looked at the net effects of all the Rana Plaza responses
56:42 and those were kind of,
56:43 you know,
56:44 a series of responses.
56:45 We can't
56:46 unbundle,
56:47 you know,
56:47 different kind of things that happened,
56:48 but we can estimate the net effects of all these responses
56:52 which were that retailers started pushing toward for better conditions
56:56 and they were prompted by both,
56:58 uh,
56:59 by both kind of,
57:00 um,
57:00 high income.
57:01 Country consumers who protested and said,
57:04 you know,
57:04 I don't wanna buy clothes from,
57:06 um,
57:06 H&M anymore if you're going to be,
57:09 um,
57:09 sourcing from factories where workers are killed.
57:12 So that was one source of the change.
57:14 But also,
57:15 um,
57:15 workers in Bangladesh themselves protested for,
57:19 uh,
57:19 better working conditions and,
57:20 and higher wages.
57:22 Those culminated in some high profile but ultimately voluntary,
57:27 um,
57:27 initiatives.
57:28 You might remember their names,
57:29 the Accord and the Alliance.
57:31 Where factories could be,
57:33 um,
57:33 you know,
57:34 could choose to be audited,
57:35 they were,
57:35 they were voluntary,
57:36 although the retailers might have pushed them to,
57:39 to do so and said,
57:40 you know,
57:40 I will only buy from you if you sign on to these,
57:43 uh,
57:43 these alliances.
57:44 So,
57:44 uh,
57:45 you know,
57:45 that was an important channel.
57:46 Even factories that didn't sign might have improved working conditions or wages
57:51 because the retailers were still pushing them even if they didn't sign
57:55 or just to compete with these,
57:56 um,
57:56 other factories that were signing and where working conditions were improving.
58:00 So you know,
58:01 again,
58:01 the net impact of all of those,
58:03 we can't disentangle what's a direct channel,
58:05 what's an indirect,
58:06 but we try to estimate the net impact of all those channels
58:10 by comparing workers in the garment industry
58:14 after Rana Plaza happened
58:16 in treated districts,
58:18 and those are districts that export,
58:20 so we're thinking that these are the ones
58:21 where the international buyers are pushing for better conditions
58:25 and comparing.
58:26 Those workers
58:27 to um
58:28 garment sector workers in districts where there
58:31 aren't export factories is the one control group
58:34 and then,
58:35 uh,
58:35 other manufacturing workers
58:37 in these um districts where they're export factories that are that are not,
58:41 um,
58:42 that are not garment factories
58:44 so that's,
58:44 um,
58:45 that's kind of our plaza,
58:46 you know,
58:46 that's our identification strategy to estimate the
58:49 plausibly causal impacts of the this.
58:51 Package of um
58:52 reforms after Rana Plaza
58:54 and the um the the line of the table in blue gives those effects
58:59 and so what we see is that wages went up by uh on average 10% after Rana Plaza
59:05 and so did working conditions.
59:06 Those are measured in standard deviations
59:08 and it's a large effect of 0.8% of a standard deviation increase of,
59:12 um,
59:13 of working conditions,
59:15 um.
59:16 So you know that's quite a striking result um
59:20 and you know overall compensation went up because both
59:23 um both wages and non-wage benefits went up,
59:27 but the other thing that we find is that
59:30 this didn't come at the expense of total employment
59:32 which you maybe were already predicting because you know
59:35 we saw the industry just kept going so that
59:37 you know some workers needed to sustain that production.
59:40 So what it looks like happened is that before Rana Plaza
59:44 employers had some degree of monopsony power,
59:46 uh,
59:46 market power over the employees,
59:49 and so there was scope to raise total compensation
59:52 if they were,
59:53 um,
59:53 adequately motivated to do so.
59:55 And indeed
59:56 the retailers pushing for better conditions after Rana
59:59 Plaza was exactly the motivation they needed,
1:00:02 so it was kind of,
1:00:03 uh,
1:00:03 it did kind of result in a net win for the for the workers.
1:00:09 So that was a um kind of that was a
1:00:11 story of the retailers kind of pushing for better conditions that
1:00:15 improved workers' lives on the ground as I mentioned of
1:00:17 course workers were advocating for better conditions so they weren't totally
1:00:21 uh uninvolved in the process but it looks like kind of a key channel was the um
1:00:26 the the retailers.
1:00:28 But
1:00:28 what I wanna ask next is the question
1:00:31 is there kind of a more bottom up approach
1:00:33 that says.
1:00:34 If we give workers information,
1:00:36 will that empower them to access jobs with better conditions?
1:00:40 Um,
1:00:41 so in this work with Laura Boudreau and Tyler McCormick,
1:00:44 we,
1:00:44 we point out the descriptive fact that when
1:00:46 we compare internal migrants in Bangladeshi garment factories to
1:00:51 local workers who grew,
1:00:53 everybody's from Bangladesh,
1:00:54 but we're comparing migrants from rural areas to local workers
1:00:57 who grew up in these areas.
1:00:59 Near the factories,
1:01:00 um,
1:01:01 when we do that,
1:01:01 we find that internal migrants are in factories with worse working conditions,
1:01:05 and this might not surprise you because,
1:01:07 you know,
1:01:08 internal migrants might be disadvantaged in,
1:01:10 in many ways,
1:01:11 but strikingly
1:01:12 they're actually,
1:01:13 if anything in factories with higher wages,
1:01:16 um,
1:01:17 so it's not just an overall disadvantage story,
1:01:19 it was,
1:01:19 you know,
1:01:20 something about the,
1:01:21 the getting into factories with worse working conditions.
1:01:24 The other key fact is that the migrants move towards better,
1:01:28 uh,
1:01:28 factories with better conditions as they as they gain experience.
1:01:32 So we argue this is consistent with a story in which
1:01:35 internal migrants were less informed about the industry as they as they began,
1:01:39 uh,
1:01:40 you know,
1:01:40 you can't know what's a good workplace,
1:01:42 uh,
1:01:42 you don't know what the factory,
1:01:43 if the factory is gonna fall down unfortunately until it does,
1:01:46 so they're less informed.
1:01:48 Local workers had more word of mouth,
1:01:49 um,
1:01:50 about where it's a good place to work.
1:01:52 Everybody knows what wages are.
1:01:53 So the,
1:01:54 the factories that were attracting migrants were actually competing on wages.
1:01:57 They just,
1:01:58 it wasn't,
1:01:59 uh,
1:01:59 efficient for them to compete on working conditions because
1:02:02 workers wouldn't even notice those investments.
1:02:04 Um,
1:02:05 but as the workers gained experience,
1:02:06 they started behaving more like locals.
1:02:09 So what the,
1:02:10 you know,
1:02:10 so this suggests that there's a learning process of migrants,
1:02:13 but that there's some welfare gain to be had
1:02:16 if you give them the information and help them,
1:02:18 uh,
1:02:18 you know,
1:02:19 help them kind of on this slow learning process.
1:02:21 Can,
1:02:21 can we speed that along?
1:02:23 So this was a descriptive paper that kind
1:02:25 of suggests that there's this learning model,
1:02:28 um,
1:02:28 but so Laura and I,
1:02:30 um,
1:02:31 decided to test that experimentally in work with um Shaquille Ahmed.
1:02:35 So,
1:02:36 um,
1:02:37 in this RCT
1:02:38 one of the treatments is what we call a,
1:02:40 uh,
1:02:40 report card for garment factories,
1:02:42 um,
1:02:43 and on the right here I show you an anonymized version,
1:02:46 um,
1:02:46 and translated.
1:02:47 Into English of the report card that that workers got
1:02:50 so to make this report card,
1:02:51 the first thing we did was we conducted a large scale,
1:02:55 uh,
1:02:55 geographically representative survey of workers in these
1:02:58 neighborhoods because there's no publicly available information,
1:03:01 um,
1:03:01 thinks about,
1:03:02 um,
1:03:03 about a representative sample of factories.
1:03:05 The kind of factories that you have audit data from aren't representative
1:03:08 and we really wanted to make sure we had information about.
1:03:11 Uh,
1:03:11 the full set of factories,
1:03:12 um,
1:03:13 so we color coded the,
1:03:15 um,
1:03:15 uh,
1:03:16 the grades that workers,
1:03:17 um,
1:03:18 that workers got.
1:03:19 We masked grades that were below median because we were worried about factories
1:03:23 discovering this and getting mad at us and the,
1:03:25 and the workers.
1:03:26 So,
1:03:26 uh,
1:03:27 if you're below median,
1:03:28 you only see the color code,
1:03:29 so at least they don't know exactly the kind of score,
1:03:32 uh,
1:03:32 that they had even though we told workers that they were in order.
1:03:35 Um,
1:03:36 so we,
1:03:37 um,
1:03:37 you know,
1:03:38 we kind of,
1:03:38 there were about 30 different survey data,
1:03:40 uh,
1:03:41 questions that went into this report card,
1:03:43 and we commissioned again like Nashi in the,
1:03:45 the comic book,
1:03:46 we commissioned,
1:03:46 uh,
1:03:47 um,
1:03:47 an illustrator to kind of make little pictures to help
1:03:49 workers that weren't fully literate to kind of understand
1:03:52 what were the things that went into these different,
1:03:55 um,
1:03:55 into these different grades.
1:03:57 Um,
1:03:58 so my time is up.
1:03:59 So,
1:03:59 um,
1:04:00 we showed the,
1:04:01 um,
1:04:01 uh,
1:04:02 we showed the,
1:04:02 um,
1:04:03 the grades for these specific measures,
1:04:05 things like not just an overall grade but employment practices,
1:04:09 uh,
1:04:09 maternity and child care,
1:04:11 physical safety and comfort,
1:04:12 so that workers that kind of cared specifically about some
1:04:15 dimension would be able to check that score as well.
1:04:18 Um,
1:04:19 there was another treatment arm that,
1:04:20 um,
1:04:21 gave workers job vacancy information that we
1:04:23 collected from HR managers of the factories,
1:04:26 and,
1:04:27 uh,
1:04:27 this is in Bungalow,
1:04:28 but you kind of get the,
1:04:29 the format of it
1:04:30 to see if,
1:04:31 um,
1:04:31 you know,
1:04:32 if you need the,
1:04:33 if this is kind of an additional tool that helps workers move towards,
1:04:36 uh,
1:04:36 factories with better wages or working conditions.
1:04:39 Um,
1:04:40 so just to sum up,
1:04:41 I'd say,
1:04:42 you know,
1:04:42 how can we think about improving working conditions?
1:04:45 There's rules for both kind of a top down approach that in,
1:04:48 uh,
1:04:48 um,
1:04:49 you know,
1:04:49 in the,
1:04:49 in a supply,
1:04:50 a long supply chain and export manufacturing,
1:04:52 buyers have an important role,
1:04:55 um,
1:04:55 but also this kind of bottom up approach,
1:04:57 um,
1:04:57 and I should mention that,
1:04:58 um,
1:04:58 that that survey is ongoing.
1:05:00 We're actually launching the in line next week,
1:05:02 so I don't have the results of that experiment,
1:05:04 but,
1:05:04 um.
1:05:05 Um,
1:05:05 I'm excited to analyze and disseminate them and to get that information out,
1:05:09 but at least kind of the descriptive paper I
1:05:11 mentioned suggests that there's this important role for,
1:05:14 uh,
1:05:14 for information.
1:05:15 Um,
1:05:16 and,
1:05:16 uh,
1:05:17 and,
1:05:17 and I'll just conclude with a very brief plug for some of my,
1:05:20 um,
1:05:20 co-authors and others,
1:05:21 um,
1:05:22 work that,
1:05:23 you know,
1:05:23 it's not either or top down or bottom up.
1:05:25 There's kind of hybrid approaches where,
1:05:27 um,
1:05:27 unions or safety committees that the factories form but are kind of
1:05:32 staffed with workers can also be
1:05:34 really effective at improving working conditions,
1:05:36 um,
1:05:37 so thank you.
1:05:38 Thank you,
1:05:39 Rachel.
1:05:42 Thanks,
1:05:42 a big thanks to all our panelists,
1:05:44 stuck on time,
1:05:46 on point,
1:05:46 right?
1:05:47 So now we have 25 minutes for questions and answers.
1:05:51 So we're gonna do
1:05:52 3 rounds.
1:05:54 Uh,
1:05:55 and I would say
1:05:57 let's
1:05:58 the question short,
1:05:59 ask a question,
1:06:00 not make a comment,
1:06:02 and then,
1:06:03 you know,
1:06:03 identify if there's a particular,
1:06:05 uh,
1:06:05 speaker you'd like to reach out to anyone on that side?
1:06:08 OK,
1:06:09 one.
1:06:10 2
1:06:11 and anyone 3,
1:06:12 right?
1:06:12 Let's do 3 right now.
1:06:15 Hi,
1:06:15 good afternoon.
1:06:15 My name is Harsh.
1:06:17 I
1:06:17 till recently had the very fun job of working with the,
1:06:21 the World Bank Gender Group on developing the strategy
1:06:24 and organizing and analyzing consultations to get content for that.
1:06:29 You,
1:06:29 Pam,
1:06:29 ended your presentation,
1:06:30 if I understood correctly,
1:06:32 questioning the merit of engaging fathers in care
1:06:35 and,
1:06:36 uh,
1:06:36 risking reducing women's autonomy
1:06:39 and,
1:06:39 uh,
1:06:40 bringing in sticky social norms.
1:06:42 Um,
1:06:43 two inputs that we heard that might go contrary to that is,
1:06:47 one is the risk of backlash,
1:06:49 increasing jobs for women,
1:06:51 while not involving men.
1:06:53 And another input we've gotten is that if
1:06:55 we want to absolutely increase wages for women,
1:06:58 bring men into those jobs,
1:06:59 then wages will go up for everyone.
1:07:01 So,
1:07:01 if you have any data or reflections on that,
1:07:03 I'd love to hear that.
1:07:06 Anybody out here?
1:07:08 right.
1:07:11 Hi,
1:07:11 thank you,
1:07:11 Caridad.
1:07:12 Also a question for Pam.
1:07:13 Yeah,
1:07:13 it has to do with whether you've looked into
1:07:16 the
1:07:18 effects of um.
1:07:20 Child care preschool separately
1:07:22 because it is so different both from the perspective of the child,
1:07:26 the family,
1:07:27 and the production of quality care,
1:07:29 uh,
1:07:30 those like the 0 to 2 and the 3 to 5 age groups
1:07:33 that,
1:07:34 um,
1:07:34 I mean at least with the evidence I've looked at the,
1:07:37 the.
1:07:38 The stories are different,
1:07:40 um,
1:07:40 you know,
1:07:41 it definitely
1:07:42 preschool,
1:07:43 it's easier to produce at scale.
1:07:46 It's more standardized.
1:07:47 Kids get benefit from interacting with another,
1:07:49 so it's easier to produce better quality in a way
1:07:52 and there's more take up by families.
1:07:53 So
1:07:54 I just sorry,
1:07:55 that was my question.
1:07:56 Great,
1:07:56 and there's one out there,
1:07:57 right?
1:07:58 So who
1:07:59 the question there,
1:07:59 um,
1:08:00 thank you so much.
1:08:01 Uh,
1:08:01 my name is Divan Shi,
1:08:02 and I'm a public policy student at University of Chicago.
1:08:05 My question is for Nishat Ananukriti Booth.
1:08:08 Uh,
1:08:08 so I was before this,
1:08:09 I was working in Haryana and I worked with Mahila Police stations
1:08:12 and also like training police force,
1:08:14 and
1:08:15 one of the biggest problem was the factor of reconciliation
1:08:18 that every time there's a crime that's committed against women,
1:08:21 the focus is on
1:08:22 putting them back into the house,
1:08:23 right?
1:08:24 Uh,
1:08:24 and I think it's also because of information asymmetry that the women don't
1:08:28 know that there is enough financial resources available,
1:08:31 like there's a self-help group or there's a one stop center that they can go to,
1:08:35 which,
1:08:35 uh,
1:08:35 is my question that I want to ask Anukriti that in your research did you explore
1:08:41 using Asha workers or Angan Mari workers to sort of bridge that gap.
1:08:44 Thank you.
1:08:48 All right,
1:08:48 let's turn to the panel.
1:08:50 OK,
1:08:51 so let me,
1:08:52 so
1:08:53 thank you for the questions.
1:08:54 Um,
1:08:55 so to this first point about,
1:08:57 um,
1:08:57 so I think if I understood the question correctly,
1:08:59 you're sort of saying there are some,
1:09:01 uh,
1:09:02 lessons we've learned from the literature on women's labor force participation
1:09:06 and you're thinking about how those would relate to the,
1:09:08 uh,
1:09:09 experiments engaging fathers within the household and early childhood care.
1:09:13 I mean,
1:09:13 I think your point is a really interesting one,
1:09:14 but I'm not sure I would interpret it the way that
1:09:17 that
1:09:18 you did.
1:09:18 So I think the first point that we worry about backlash,
1:09:21 I think it's exactly
1:09:22 the sort of unintended consequences that is the thing that we should
1:09:26 worry about when we think about engaging fathers in the household.
1:09:29 So many of us come
1:09:30 from this sort of
1:09:31 wealthy country mindset of
1:09:33 of household dynamics where fathers are already.
1:09:36 Involved
1:09:37 and I think it's easy to think about that
1:09:39 in terms of net benefits for child development potentially
1:09:43 and to miss the sort of unintended consequence of having men
1:09:46 enter a female dominated space when in an environment where men
1:09:50 have a lot of power relative to women
1:09:52 and I also think that
1:09:54 we sort of
1:09:55 there's this other unintended consequence of fathers
1:09:59 who aren't.
1:10:00 Paying a lot of attention to their kids to begin with may
1:10:03 not be very good at it,
1:10:05 and the wrong intervention may in fact be worse than no intervention at all.
1:10:09 So I think it's a similar story of
1:10:11 unintended consequences and backlash that we are wary of
1:10:15 now.
1:10:15 I think your point about
1:10:16 within the labor force we see,
1:10:18 you know,
1:10:18 and it's difficult to get causality,
1:10:20 but in general
1:10:21 male dominated sectors have higher wages.
1:10:24 Uh,
1:10:24 and get more credit.
1:10:25 I mean,
1:10:25 I think that's,
1:10:26 that
1:10:27 is a very interesting
1:10:29 phenomenon,
1:10:30 but I'm not sure how it translates into the domain of
1:10:32 domestic responsibilities within the household and that these are things that are
1:10:36 not,
1:10:36 there's no market wage for how you do the dishes anyway,
1:10:39 and so I'm not sure there's a natural,
1:10:41 I wish that there were,
1:10:42 uh,
1:10:43 but,
1:10:43 uh,
1:10:44 not that I ever do the dishes,
1:10:45 but,
1:10:46 um,
1:10:46 but I,
1:10:47 but I think I'm not sure that there's a parallel with that one,
1:10:49 but.
1:10:49 I think the first point is,
1:10:50 is very well taken,
1:10:51 but,
1:10:51 but I would interpret it that we should be thinking more about the backlash
1:10:55 that's specific to the within the household context.
1:10:57 Um,
1:10:58 on the second point,
1:10:59 uh,
1:10:59 uh,
1:11:00 about 0 to 2 versus 3 to 5,
1:11:02 I mean,
1:11:03 you are right,
1:11:04 they are quite different.
1:11:06 Uh,
1:11:06 I'm speaking so in my own systematic review and some
1:11:08 of the other systematic reviews that have come out recently,
1:11:10 in general,
1:11:11 we still see that,
1:11:13 uh,
1:11:14 while it.
1:11:15 Is so access is very different between those two,
1:11:19 but I think in terms of
1:11:20 interventions being generally not bad for children,
1:11:23 that's a regularity we see on most
1:11:26 classes of outcomes for young kids
1:11:28 and for older kids.
1:11:28 And so even though we worry a lot about quality
1:11:31 both in daycare and in preschool.
1:11:34 And with some caveats about the set of
1:11:38 programs that are evaluated may not be representative of the broader market,
1:11:42 but in general what we see is that in both of those domains it
1:11:46 looks like center-based care is at least not bad and often weekly or.
1:11:50 Or strongly good for children and so I'm not sure there's
1:11:54 as stark of a discontinuity.
1:11:55 I think there's also a uh a difference
1:11:58 in terms of how they relate to
1:11:59 women's labor force participation because with preschool,
1:12:02 if you also have even younger kids,
1:12:04 then it has very little impact on women's labor force participation.
1:12:07 Shit.
1:12:10 Was it for me or
1:12:11 both of you,
1:12:13 if I can start,
1:12:14 uh,
1:12:14 so I think I agree what you just said that that is true,
1:12:17 that often if there is,
1:12:19 say,
1:12:19 a dispute within the household or in,
1:12:21 you know,
1:12:22 issue with domestic violence,
1:12:23 uh,
1:12:23 even the,
1:12:24 the police station or,
1:12:26 you know,
1:12:26 anyone who's involved try to sort of make sure that
1:12:28 the woman basically just goes back to the household,
1:12:31 and I think it just reflects the fact that
1:12:33 the outside options for women are pretty bad.
1:12:36 Right,
1:12:36 so,
1:12:37 uh,
1:12:37 one is if you're not working,
1:12:38 if you're not financially independent,
1:12:40 then that creates a constraint.
1:12:42 Um,
1:12:42 the other alternative might be to go back to,
1:12:44 let's say,
1:12:45 to live with your NATO family or,
1:12:46 you know,
1:12:46 parents for a little while.
1:12:47 And again there is a lot of social stigma in this context,
1:12:51 especially in places like Haryana,
1:12:52 and that's really something that is frowned upon.
1:12:54 And in fact there's a reason why you know your daughter is
1:12:56 supposed to just in pretty local societies just be given away,
1:12:59 and then,
1:13:00 you know,
1:13:00 the interaction between NATO families and.
1:13:02 Marital families is supposed to be very low,
1:13:04 so I think this,
1:13:06 even though this is sort of an outcome we don't want,
1:13:08 it essentially reflects the social norms that are
1:13:11 prevalent and the constraints that women face.
1:13:13 So in that sense,
1:13:14 you know,
1:13:15 if the alternative is just really so bad,
1:13:17 then you know then.
1:13:18 Even though this may not be the best outcome,
1:13:20 you know,
1:13:20 everybody's trying to sort of do that,
1:13:22 so I think the way we would try to
1:13:24 that might become weaker if women are more likely to participate in the economy,
1:13:29 work,
1:13:29 and have financial independence,
1:13:31 and then of course,
1:13:31 you know how social norms change over time.
1:13:34 Uh,
1:13:34 and I think,
1:13:35 and I let me add to that,
1:13:36 and then the second point,
1:13:38 uh,
1:13:38 you raised was about ASHA workers.
1:13:39 So in that,
1:13:40 in the project that I discussed,
1:13:42 we did not because we were working with the local
1:13:44 family planning clinic and we could distribute the vouchers ourselves.
1:13:47 But in ongoing work we have now actually gone
1:13:49 back to Jaunpur and we are working with the mothers-in-law
1:13:52 and,
1:13:52 uh,
1:13:53 and the daughters.
1:13:54 In law and we're trying to,
1:13:55 you know,
1:13:56 work with health workers and
1:13:57 it's interesting how even though we think that
1:13:59 Ahas definitely have a lot of presence,
1:14:02 uh,
1:14:02 if you ask how many people have actually
1:14:04 visited your house to discuss family planning,
1:14:06 it's actually not that high,
1:14:07 so it's mainly focused on
1:14:08 childhood immunization and you know things like that,
1:14:11 but hopefully we'll see how that goes.
1:14:14 It's a great question,
1:14:15 and
1:14:16 I don't think I have an answer based on my research,
1:14:19 but I'll give you an answer based on my working on this topic for
1:14:22 a pretty long time.
1:14:23 I think you don't want to think about all the cases in one category,
1:14:26 right?
1:14:26 So you want to
1:14:27 pick up,
1:14:28 like,
1:14:28 let's say there is a case where there's a very severe beating.
1:14:32 And
1:14:33 in the last I would say 10 years,
1:14:36 the cost of reporting has gone down.
1:14:38 So when you report,
1:14:39 there is a pretty specific protocol that officers
1:14:42 try to follow depending on what their constraints are,
1:14:45 and their immediate response in that case typically
1:14:48 is like you go and arrest the husband.
1:14:51 But there's also Supreme Court guidelines about reconciliation
1:14:55 that you don't want to kind of just go and do this
1:14:57 because there would be absolutely no reconciliation after that given also from
1:15:02 what we know about the family,
1:15:03 mother-in-laws,
1:15:04 father-in-laws,
1:15:05 things like that.
1:15:06 So
1:15:07 at least in the context of Bihar where I have spent most time,
1:15:11 a lot of these officers focus on reconciliation.
1:15:13 That is kind of like almost their primary goal,
1:15:16 but
1:15:17 there has been a pretty steady rise of counseling.
1:15:20 So almost every police station
1:15:22 is supposed to have a counselor,
1:15:24 but that counselor could be a police officer himself.
1:15:27 They don't necessarily have an external counselor,
1:15:30 but in that case they would bring the husbands and the family and
1:15:34 kind of make them go through some counseling that this is not OK.
1:15:37 And if you do this next time,
1:15:39 I'm going to arrest you and put you in jail.
1:15:40 So I think it varies a lot on case by case.
1:15:44 So that's all I can say.
1:15:47 Right,
1:15:47 let's take some more questions.
1:15:48 Let's start again from that side.
1:15:50 If someone has a question,
1:15:52 now
1:15:53 in the center,
1:15:54 the two out here,
1:15:55 let's,
1:15:56 let's get the mic here,
1:15:57 please at the back.
1:16:00 Hey,
1:16:00 uh,
1:16:01 I'm Isabella Brati.
1:16:02 Oops,
1:16:02 sorry,
1:16:03 uh,
1:16:04 I'm from Ernst and Young's Quantitative Economics and Statistics Unit,
1:16:07 and I have a question
1:16:09 about.
1:16:11 Um,
1:16:11 so,
1:16:11 so we heard that these interventions work.
1:16:14 Um,
1:16:15 do you know a way
1:16:17 how to make these interventions to become a budget line with a number next to them?
1:16:21 Do you,
1:16:21 is there a dialogue with governments
1:16:23 to make them,
1:16:25 um.
1:16:27 Part of the process and and help them uh develop their agency.
1:16:32 Um,
1:16:33 in these countries
1:16:35 and my other,
1:16:35 I'm sorry,
1:16:36 I have two questions yet.
1:16:37 The second one is a tiny one though,
1:16:39 so we are looking at,
1:16:40 um,
1:16:41 women's,
1:16:41 um,
1:16:42 standing on the job market and their situation.
1:16:45 Do you know of studies that looked at the long
1:16:48 term impact of these
1:16:50 interventions,
1:16:51 for example,
1:16:52 um,
1:16:53 pension poverty,
1:16:54 uh,
1:16:55 the,
1:16:55 the gap there between,
1:16:56 uh,
1:16:56 men and women.
1:16:58 That
1:16:59 that's it thank you and there's another
1:17:01 right there.
1:17:05 Thank you.
1:17:06 I am.
1:17:06 That's very loud,
1:17:07 uh,
1:17:07 Nicole Golden.
1:17:08 I'm currently a non-resident senior fellow at the Atlantic Council,
1:17:11 among some other consulting hats.
1:17:13 Uh,
1:17:13 quick question for,
1:17:13 is it Anuriti if I pronounce that right?
1:17:15 I'm just curious if you
1:17:18 looked at,
1:17:19 um,
1:17:20 any
1:17:22 impacts,
1:17:22 um,
1:17:23 in your study on
1:17:25 the women on
1:17:27 picking up any
1:17:28 educational or income generating activities,
1:17:31 um,
1:17:31 as a secondary or other impact,
1:17:33 um,
1:17:34 that you saw.
1:17:34 Thanks.
1:17:36 Any questions on this side of the room?
1:17:39 Uh,
1:17:40 2 of them,
1:17:40 OK.
1:17:42 Hi,
1:17:42 um,
1:17:43 thank you for the presentations.
1:17:45 Um,
1:17:46 I'm Gillam Sarkar and I'm a PhD student at American University.
1:17:50 I have a quick question for Professor Prakash.
1:17:54 Um,
1:17:56 so among your findings there was that
1:17:59 police patrols had no impact on overall street harassment.
1:18:04 Can you,
1:18:05 um,
1:18:05 like give us some of your insights that why
1:18:08 that may be the case?
1:18:10 Thank you.
1:18:13 Did you have a question right next to her?
1:18:15 All right.
1:18:17 Hi,
1:18:17 my name is Reva Restak.
1:18:19 I'm also a PhD student at AU and I used to be an RA at CGD.
1:18:24 Uh,
1:18:24 my question is about the garment factory worker
1:18:28 scorecards.
1:18:30 In light of recent Supreme Court decisions in the US,
1:18:33 I'm wondering if there's an equivalent in the US,
1:18:36 uh,
1:18:37 to
1:18:38 find de facto conditions about factory workers here
1:18:42 moving forward with the NLRB decision.
1:18:45 Thanks.
1:18:47 Great.
1:18:47 Uh,
1:18:48 should we turn back to our panel?
1:18:50 Rachel,
1:18:50 why don't I start with you?
1:18:52 Um,
1:18:53 uh,
1:18:54 thanks,
1:18:54 yeah,
1:18:54 so it's a,
1:18:55 it's a great question,
1:18:56 and I,
1:18:56 I confess to not being,
1:18:58 um,
1:18:58 not being very up on the,
1:18:59 on the US context,
1:19:01 um.
1:19:02 Um,
1:19:02 but,
1:19:03 but,
1:19:03 but I think that
1:19:05 the,
1:19:05 I mean,
1:19:06 I think that we in the US we have some kind of institutions that,
1:19:10 that do do kind of provide similar information already,
1:19:13 things like Glassdoor,
1:19:14 and so,
1:19:15 um,
1:19:16 and actually this relates to the question about scaling up so that,
1:19:19 um,
1:19:20 in,
1:19:20 you know,
1:19:20 when,
1:19:20 when we kind of think about how,
1:19:22 um,
1:19:22 you know,
1:19:23 such kind of these information repositories might.
1:19:25 Be scaled up.
1:19:26 One possibility is that,
1:19:28 you know,
1:19:29 once they,
1:19:30 once we kind of have this information that the garment
1:19:32 factories will want to do this themselves because that kind of
1:19:35 helps,
1:19:36 you know,
1:19:36 get workers in factories that are better matches for their specific preferences
1:19:40 and so that might be,
1:19:41 you know,
1:19:41 kind of,
1:19:42 um,
1:19:42 you know,
1:19:43 this might be something that industries would want to do on its own
1:19:46 or,
1:19:46 you know,
1:19:46 kind of a more an NGO or something that.
1:19:48 It's like,
1:19:49 like Glassdoor that's kind of funded,
1:19:50 um,
1:19:51 um,
1:19:51 you know,
1:19:52 kind of funded through some other external source,
1:19:54 and so I think those,
1:19:55 um,
1:19:56 I,
1:19:56 I think that,
1:19:57 you know,
1:19:57 that there we do see evidence of kind of demand for
1:20:00 this information that's kind of being provided in the US,
1:20:03 but
1:20:03 you know,
1:20:04 the it's possible that there is a role for the government in light of kind of,
1:20:07 you know,
1:20:08 specific legal,
1:20:09 um,
1:20:09 you know,
1:20:10 kind of specific legal changes.
1:20:12 Sure,
1:20:14 so I think one of the question was about,
1:20:15 uh,
1:20:16 take up,
1:20:16 right?
1:20:17 Uh you talked about
1:20:18 these interventions and take up by I guess the policymakers is that.
1:20:23 So,
1:20:23 uh,
1:20:24 I'll give you like a few,
1:20:25 I,
1:20:25 I think a lot depends on the timing
1:20:27 when you go and talk to them.
1:20:28 So suppose you go and talk to them towards the fag end of the,
1:20:31 when the
1:20:32 government's gonna change,
1:20:33 there's absolutely,
1:20:34 there's no room for conversation.
1:20:36 Uh,
1:20:37 so
1:20:38 I think the first key is like how do you
1:20:40 work together,
1:20:41 kind of co-create that policy.
1:20:43 I think that plays a very important role in terms of
1:20:47 whether it's
1:20:48 the person who is in charge or someone comes after that.
1:20:51 I think that co-creation has been pretty helpful.
1:20:53 I'll give you two examples where
1:20:55 in the
1:20:56 paper in Hyderabad where we found these results and we said,
1:20:59 look,
1:20:59 you know,
1:21:00 can we push for say surprise component that's uniform
1:21:03 policing because this is what the results show.
1:21:06 And it was a no starter.
1:21:08 Like,
1:21:08 no,
1:21:08 this is not gonna
1:21:10 work well with the government,
1:21:12 and I said that's
1:21:14 fine
1:21:14 if there's no room,
1:21:15 but he's like,
1:21:16 tell me a bigger lesson or what can we do?
1:21:18 And I said,
1:21:19 well,
1:21:20 the second part of the lab experiment talks about attitudes,
1:21:23 so you know,
1:21:23 can we think about a training program?
1:21:25 And he's like,
1:21:25 oh,
1:21:26 that's a no brainer,
1:21:26 it's very easy to implement.
1:21:28 So you kind of like you have to figure out what's the right message you want to talk,
1:21:31 and I think the takeup becomes easier.
1:21:33 And on the other hand,
1:21:34 in Bihar,
1:21:35 when we didn't even have results,
1:21:36 this is early January when I went with some
1:21:38 descriptives to the administrative head of the state,
1:21:42 and he looked at these slides and he said,
1:21:44 Oh,
1:21:44 can we implement this in the academy?
1:21:46 And I said,
1:21:46 Oh,
1:21:46 we don't have any evidence.
1:21:47 He's like,
1:21:48 no,
1:21:48 it doesn't matter,
1:21:49 so it looks good.
1:21:50 And he immediately made a call to the police academy and he said,
1:21:53 Can you implement this curriculum?
1:21:54 So I have like two examples.
1:21:56 Uh,
1:21:56 I have another example from education work where I worked in Zambia and uh.
1:22:02 There has been take up in other countries
1:22:04 and you have this question about why we don't
1:22:06 find and I think it's all about deterrence.
1:22:09 So the number of times you make an arrest,
1:22:12 I think if that number is not high,
1:22:14 it's just not going to be enough and I think
1:22:18 that project was a big learning because we had lots
1:22:21 of conversation with a very open minded police commissioner,
1:22:25 and he said look.
1:22:27 Sexual harassment is very important.
1:22:29 I care about this.
1:22:30 Is this my number one priority?
1:22:31 And the answer is no.
1:22:33 Number one is like,
1:22:34 you know,
1:22:34 in Hyderabad,
1:22:35 the religious tensions becomes the number one.
1:22:38 Like
1:22:39 it's there.
1:22:40 They have resources,
1:22:41 everything,
1:22:42 but that's not number one,
1:22:43 right?
1:22:43 So and I think one has to understand the constraint,
1:22:46 but they had a program.
1:22:48 It was well monitored,
1:22:49 and I think there was a lot of learning,
1:22:51 but I think it's a lot about veterans and what's the frequency of these arrests.
1:22:55 Thanks Nishid uh Anu.
1:22:57 Yeah,
1:22:57 so to answer your question,
1:22:59 uh,
1:22:59 we don't,
1:23:00 so our end line was 10 months after,
1:23:02 you know,
1:23:02 we,
1:23:02 the intervention started,
1:23:03 and during that time period we don't find any impact on education or employment.
1:23:08 So education we were expecting because
1:23:10 typically once,
1:23:11 you know,
1:23:11 uh,
1:23:12 women are married,
1:23:12 it's,
1:23:13 it's they're not going back to the school to such a large extent,
1:23:16 uh,
1:23:16 for the labor market again it could be that,
1:23:18 you know,
1:23:18 you need to wait a bit longer because,
1:23:20 uh,
1:23:20 we do see a decrease in pregnancy,
1:23:22 but it's not.
1:23:23 It depends on for how long that,
1:23:24 you know,
1:23:25 the birth spacing goes up,
1:23:26 but at at least in our study we don't find anything.
1:23:29 Uh,
1:23:29 we are now going back,
1:23:31 um,
1:23:32 to the sample and trying to see whether there are any longer term impacts,
1:23:35 for instance,
1:23:35 on mental health,
1:23:36 you know,
1:23:36 because a lot of,
1:23:37 we got a lot of questions about,
1:23:38 you know,
1:23:39 given that you are so socially isolated,
1:23:41 it may actually impact women's mental health,
1:23:42 and so we are collecting data on that and
1:23:45 hopefully we'll be able to
1:23:46 say something on that,
1:23:48 uh,
1:23:48 and to on the question of the impact of interventions.
1:23:51 I think
1:23:52 at least in my experience I find that researchers are perhaps not really good at
1:23:56 working with policymakers or engaging with policymakers
1:23:59 and focusing on actually trying to
1:24:01 uh you know get the research we do turned into policy,
1:24:04 you know,
1:24:04 maybe the focus sometimes is more on let's say
1:24:06 publications and and the audience can be very different.
1:24:10 um
1:24:10 I think in my experience what I found is of course
1:24:13 there are projects where you're directly working with the policy maker,
1:24:15 right?
1:24:16 So that's a different opportunity they get to see how you're doing.
1:24:19 Research and maybe the observe impact in a much more direct,
1:24:22 you know,
1:24:22 immediate way and then it's much easier
1:24:25 when you're not working with the policymaker.
1:24:27 Then you really have to figure out how do I
1:24:29 convey the findings that I have for those policymakers.
1:24:32 They are probably not going to read the American Economic Review and learn about
1:24:37 research.
1:24:37 So I think we need to find ways to communicate and disseminate results to a
1:24:41 broader audience.
1:24:42 So for instance,
1:24:43 in the case of India,
1:24:44 there is
1:24:45 ideas for India.
1:24:46 It's a.
1:24:46 From where you can publish your
1:24:48 research findings for maybe a a policy audience and I do know several
1:24:52 uh you know,
1:24:53 administrative officers who actually read work and
1:24:55 then when I published something they've communicated
1:24:57 so I think that's one the other,
1:24:59 at least
1:25:00 being at the bank we do end up working with,
1:25:03 uh,
1:25:03 you know,
1:25:04 governments and,
1:25:04 and that I think either on active projects where it is already being scaled up
1:25:09 and then research helps figure out exactly the nuances
1:25:12 of that project or how to improve implementation.
1:25:15 and that's helpful.
1:25:16 And the last thing I would say is I've also found that
1:25:19 yes,
1:25:19 presenting a research heavy,
1:25:21 you know,
1:25:22 causally identified study,
1:25:23 yes,
1:25:23 is useful,
1:25:24 but sometimes,
1:25:25 you know,
1:25:25 a very basic exercise is sometimes even more useful.
1:25:29 So I once presented the baseline findings of
1:25:33 a very basic survey to a state government in India,
1:25:35 and
1:25:36 it was really,
1:25:36 I find,
1:25:37 impactful because.
1:25:38 They didn't have much data on on the types of things that we were collecting data on,
1:25:42 and I think
1:25:42 that itself,
1:25:43 you know,
1:25:44 helped them
1:25:45 be more open to the idea,
1:25:46 OK,
1:25:46 now we can do research and maybe do an impact evaluation,
1:25:49 which they were much more than,
1:25:50 you know,
1:25:51 receptive to afterwards.
1:25:52 Thank you,
1:25:53 Anu,
1:25:53 and thank you everyone for this really,
1:25:55 really
1:25:56 great discussion
1:25:57 and to the audience for the very useful questions.
1:26:00 Thanks everyone
1:26:01 and uh.
1:26:03 Our next session
1:26:04 starts in 15 minutes and uh that's gonna be an
1:26:08 economic inclusion of the year.
1:26:09 So thanks again.
- add-style
- lp-body-content
A video recording of the second session—Day 2—of The Annual Bank Conference on Development Economics 2024 "The Great Incoherence: Growth and Human Development in An Era of Stagnation." This session discusses "Norms and Other Constraints to Women’s Economic Inclusion."
Papers discussed in this session are:
- Paper 1: Early Childhood Interventions and Women’s Empowerment (Pam Jakiela, Williams College)
- Paper 2: Women’s Social Networks (S Anukriti, World Bank)
- Paper 3: Sexual Harassment in Public Space: Evidence and Future Directions (Nishith Prakash, Northeastern University)
- Paper 4: Policy Solutions to Improve Working Conditions in Export Manufacturing(Rachel Heath, University of Washington)